posts
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Observability Design for the AI Era — Reconciling PII Protection With AI Searchability, and Driving Self-Healing
Part 1 laid out four monitoring axes (application / infrastructure / CI / LLM) and the shape each one ends up in. Part 2 picks up where the data actually flows: it's production data, with PII in it. This post is about a multi-layer PII design that hashes at both write and search time with the same function, an integration surface where humans (web dashboard) and AI (MCP) share the same backend, and how all of that becomes the real driver of Self-Healing — running from CI failure to PR proposal end-to-end.
Observability Design for the AI Era — Application / Infrastructure / CI / LLM, Each in Its Own Shape
The previous code-graph series was about reshaping a static analysis graph so AI could query it. The same kind of reshaping is needed on the observability side. This post walks through four axes — application / infrastructure / CI / LLM — and the deliberately different shapes each one ends up in. The design judgments worth calling out: computing Gemini cost client-side instead of from billing API, sending Claude Code OTel straight to BigQuery instead of Loki, and shipping CI logs via post-hoc pull instead of webhook push.
The Heart of the AI Harness: A Knowledge Graph of the AI, by the AI, for the AI
Series Part 2: how we built cortex-product-graph (cpg) — a unified knowledge graph of code, docs, DB schemas, and infrastructure for the cortex AI platform. Build pipeline with JSDoc/Pulumi/docs as SSoT, plus the Runbook tool-design pattern that guides AI through the graph.
Graph RAG Isn't a One-Shot Anymore — The Case for Agentic Graph RAG MCPs
Vector RAG and one-shot Graph RAG both flatten the search step. Agentic Graph RAG hands the graph to an LLM as an MCP and lets it traverse relationships iteratively.
Cutting Self-Built MCP Server Token Usage by 90% — The Parking Pattern
MCP responses fill the context window fast. The parking pattern stores heavy payloads externally and returns only a key — about 90% token savings in production.
Bridging 'I Want to Build' and 'I Want to Publish Safely' for Non-Engineers — Sandbox MCP
Non-engineers can build AI apps, but publishing safely is still gated by engineers. Sandbox MCP gives them a one-command path to deploy Web/API/DB/Cron with guardrails.
Democratizing Internal Data — Building an MCP Server That Lets You Search 991 Tables in Natural Language
Internal data lives across 15 schemas, 991 tables, 11 SQL DBs and 6 MongoDBs. DB Graph MCP lets Claude search and query the whole thing in natural language.